bagelhole/gcp-cloud-functions

GCP Cloud Functions

Build and deploy event-driven serverless applications with Google Cloud Functions (Gen1 and Gen2).

When to Use

• Processing webhooks, API endpoints, or lightweight HTTP backends
• Reacting to events from Pub/Sub, Cloud Storage, Firestore, or Eventarc
• Running scheduled tasks (cron) without maintaining a server
• Building data-processing pipelines triggered by file uploads
• Prototyping microservices before committing to Cloud Run or GKE

Prerequisites

• Google Cloud SDK (gcloud) installed and authenticated
• APIs enabled: Cloud Functions, Cloud Build, Artifact Registry, Cloud Run (Gen2)
• IAM role roles/cloudfunctions.developer (or roles/run.developer for Gen2)

gcloud services enable cloudfunctions.googleapis.com cloudbuild.googleapis.com \
  artifactregistry.googleapis.com run.googleapis.com eventarc.googleapis.com

Gen1 vs Gen2 Comparison

| Feature | Gen1 | Gen2 (recommended) | |---------|------|---------------------| | Runtime | Cloud Functions infra | Built on Cloud Run | | Max timeout | 9 minutes | 60 minutes | | Max memory | 8 GB | 32 GB | | Concurrency | 1 request/instance | Up to 1000/instance | | Traffic splitting | No | Yes | | Eventarc triggers | No | Yes |

Deploy an HTTP Function (Gen2)

# Python HTTP function
gcloud functions deploy hello-http \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-http --allow-unauthenticated \
  --entry-point=hello_http \
  --memory=256Mi --timeout=60s \
  --min-instances=0 --max-instances=100 \
  --set-env-vars=APP_ENV=production --source=.

# Node.js HTTP function
gcloud functions deploy hello-node \
  --gen2 --region=us-central1 --runtime=nodejs20 \
  --trigger-http --allow-unauthenticated \
  --entry-point=helloNode --memory=256Mi --source=.

Deploy a Pub/Sub Triggered Function

gcloud pubsub topics create order-events

gcloud functions deploy process-order \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-topic=order-events \
  --entry-point=process_order \
  --memory=512Mi --timeout=120s --retry \
  --service-account=order-processor@${PROJECT_ID}.iam.gserviceaccount.com \
  --source=.

Deploy a Cloud Storage Triggered Function

gcloud functions deploy process-upload \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
  --trigger-event-filters="bucket=my-upload-bucket" \
  --entry-point=process_upload \
  --memory=1Gi --timeout=300s --source=.

Deploy a Scheduled Function

gcloud functions deploy daily-cleanup \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-http --no-allow-unauthenticated \
  --entry-point=daily_cleanup --source=.

gcloud scheduler jobs create http daily-cleanup-job \
  --schedule="0 2 * * *" \
  --uri="https://us-central1-${PROJECT_ID}.cloudfunctions.net/daily-cleanup" \
  --http-method=POST \
  --oidc-service-account-email=scheduler-sa@${PROJECT_ID}.iam.gserviceaccount.com \
  --location=us-central1

Python Function Examples

# main.py
import functions_framework
import base64, json
from flask import jsonify
from google.cloud import firestore

@functions_framework.http
def hello_http(request):
    """HTTP Cloud Function."""
    name = request.args.get("name", "World")
    return jsonify({"message": f"Hello, {name}!", "status": "ok"}), 200

@functions_framework.cloud_event
def process_order(cloud_event):
    """Triggered by a Pub/Sub message."""
    data = base64.b64decode(cloud_event.data["message"]["data"]).decode("utf-8")
    order = json.loads(data)
    db = firestore.Client()
    db.collection("orders").document(order["id"]).set({
        "status": "processing", "items": order["items"], "total": order["total"],
    })

@functions_framework.cloud_event
def process_upload(cloud_event):
    """Triggered when a file is uploaded to Cloud Storage."""
    data = cloud_event.data
    bucket_name, file_name = data["bucket"], data["name"]
    if not file_name.lower().endswith((".png", ".jpg", ".jpeg")):
        return
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image(source=vision.ImageSource(
        gcs_image_uri=f"gs://{bucket_name}/{file_name}"))
    labels = [l.description for l in client.label_detection(image=image).label_annotations]
    print(f"Labels for {file_name}: {labels}")

# requirements.txt
functions-framework==3.*
google-cloud-firestore==2.*
google-cloud-storage==2.*
google-cloud-vision==3.*
flask>=2.0

Node.js Function Examples

// index.js
const functions = require("@google-cloud/functions-framework");

functions.http("helloNode", (req, res) => {
  const name = req.query.name || "World";
  res.json({ message: `Hello, ${name}!`, status: "ok" });
});

functions.cloudEvent("processMessage", (cloudEvent) => {
  const data = Buffer.from(cloudEvent.data.message.data, "base64").toString();
  console.log(`Processing: ${JSON.parse(data)}`);
});

Managing Deployed Functions

gcloud functions list --gen2 --region=us-central1
gcloud functions describe hello-http --gen2 --region=us-central1
gcloud functions logs read hello-http --gen2 --region=us-central1 --limit=50
gcloud functions delete hello-http --gen2 --region=us-central1 --quiet

# Update env vars without redeploying code
gcloud functions deploy hello-http --gen2 --region=us-central1 \
  --update-env-vars=APP_ENV=staging

# Test locally before deploying
functions-framework --target=hello_http --port=8080

Terraform Configuration

resource "google_cloudfunctions2_function" "api" {
  name     = "hello-http"
  location = "us-central1"

  build_config {
    runtime     = "python312"
    entry_point = "hello_http"
    source {
      storage_source {
        bucket = google_storage_bucket.source.name
        object = google_storage_bucket_object.source.name
      }
    }
  }

  service_config {
    min_instance_count    = 0
    max_instance_count    = 100
    available_memory      = "256Mi"
    timeout_seconds       = 60
    service_account_email = google_service_account.fn.email
    environment_variables = { APP_ENV = "production" }
  }
}

resource "google_cloud_run_service_iam_member" "invoker" {
  location = google_cloudfunctions2_function.api.location
  service  = google_cloudfunctions2_function.api.name
  role     = "roles/run.invoker"
  member   = "allUsers"
}

resource "google_cloudfunctions2_function" "processor" {
  name     = "process-order"
  location = "us-central1"

  build_config {
    runtime     = "python312"
    entry_point = "process_order"
    source {
      storage_source {
        bucket = google_storage_bucket.source.name
        object = google_storage_bucket_object.source.name
      }
    }
  }

  service_config {
    max_instance_count    = 50
    available_memory      = "512Mi"
    timeout_seconds       = 120
    service_account_email = google_service_account.fn.email
  }

  event_trigger {
    trigger_region = "us-central1"
    event_type     = "google.cloud.pubsub.topic.v1.messagePublished"
    pubsub_topic   = google_pubsub_topic.orders.id
    retry_policy   = "RETRY_POLICY_RETRY"
  }
}

Troubleshooting

| Symptom | Cause | Fix | |---------|-------|-----| | PERMISSION_DENIED on deploy | Missing Cloud Build or Artifact Registry perms | Grant roles/cloudbuild.builds.builder to Cloud Build SA | | Function deploys but returns 403 | Missing roles/run.invoker for Gen2 | Add --allow-unauthenticated or grant invoker role | | Cold start latency > 5s | Large dependencies or no min instances | Set --min-instances=1; reduce deps; use lazy imports | | Pub/Sub messages redelivered | Function errors or times out | Increase --timeout; fix error handling; add dead-letter topic | | Build failed during deploy | Syntax error or missing dependency | Check gcloud builds log; verify requirements.txt | | Cannot connect to VPC resource | Function not on VPC connector | Add --vpc-connector=my-connector to deploy |

Related Skills

• gcp-networking - VPC connectors for accessing private resources from functions
• gcp-cloud-sql - Connecting Cloud Functions to managed databases
• terraform-gcp - Deploy Cloud Functions with Infrastructure as Code
• gcp-gke - When workloads outgrow serverless and need Kubernetes